RNA_Content_Analysis_SPARQ_12122016
nathanbrandt
2017-01-09
Experiment overview
Write a detailed description of your experiment here including the goal of the analysis and your interpretation of the results.
If you still see this text it means that you have not described the experiment and whatever follows is meaningless. ###############################
This code is designed for use with the Accuri flow cytometer, which is equiped with the following lasers and filters
- Blue laser (488 nm)
- FL1 filter = 514/20nm GFP
FL3 filter = 575/25nm YFP
- Yellow/green laser (552 nm)
- FL2 filter = 610/20nm mCherry, dtomato
FL4 filter = 586/15nm DsRed
Requirements
In order to run this code you need:
+ to predefine your gates using the gating.R script
+ the gates.Rdata workspace, which contains the gates used in this script
+ the path of the directory(ies), given the variable names dir1, dir2… that contain .fcs files named A01.fcs, A02.fcs, A03.fcs…
+ a tab delimited sample sheet in each directory with the following rows:
+ the variable names are changed in chunk 2 named “Variable Names”
Output
This script generates a summary of results followed by quality control plots.
1 Step 1: Load relevant libraries
# This is a function that just makes sure you have a package, or installs it for you without prompting
requireInstall <- function(packageName,isBioconductor=F) {
if ( !try(require(packageName,character.only=T)) ) {
print(paste0("You don't have ",packageName," accessible, ",
"I'm gonna install it"))
if (isBioconductor) {
source("http://bioconductor.org/biocLite.R")
biocLite(packageName)
} else {
install.packages("packageName", repos = "http://cran.us.r-project.org")
}
}
return(1)
}
#Load libraries
requireInstall("flowCore",isBioconductor=T)## Loading required package: flowCore## [1] 1requireInstall("flowViz",isBioconductor=T)## Loading required package: flowViz## Loading required package: lattice## [1] 1requireInstall("flowStats")## Loading required package: flowStats## Loading required package: fda## Loading required package: splines## Loading required package: Matrix##
## Attaching package: 'Matrix'## The following object is masked from 'package:flowCore':
##
## %&%##
## Attaching package: 'fda'## The following object is masked from 'package:graphics':
##
## matplot## Loading required package: cluster## Loading required package: flowWorkspace## Loading required package: ncdfFlow## Loading required package: RcppArmadillo## Loading required package: BH## Loading required package: gridExtra## [1] 1requireInstall("Hmisc")## Loading required package: Hmisc## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'Hmisc'## The following object is masked from 'package:gridExtra':
##
## combine## The following objects are masked from 'package:base':
##
## format.pval, round.POSIXt, trunc.POSIXt, units## [1] 1requireInstall("reshape2")## Loading required package: reshape2## [1] 1requireInstall("ggplot2")## [1] 1requireInstall("flowWorkspace")## [1] 1requireInstall("ggcyto", isBioconductor=T)## Loading required package: ggcyto## [1] 1requireInstall("gridExtra")## [1] 12 Step 2: Read in .fcs files, an Rdata file containing the gates sample sheet(s) that contains four columns with
- column1 = Well
- column2 = Strain
- column3 = Staining
- column4 = Media
- column5 = Userdefined
#Read in all data for analysis. Data should be in individual directories that contain .fcs files and a corresponding sample sheet with a generic format. FCS file names should be unaltered e.g AO1.fcs, A02.fcs, ...H12.fcs
#An abitrary number of directories can be used named dir1, dir2, dir3...with a corresponding flowData.1, flowData.2, flowData.3...and sample.sheet.1, sample.sheet.2, sample.sheet.3...
#load the Rdata file containing the gates
#load("gates.Rdata")
#Define the directory, or directories, containing your .fcs files using absolute path names
dir1 <- "/Users/nathanbrandt/Google Drive/Transfer_Folder/RNA_Content_Analysis_SPARQ/mRNA_VariedMedia_KOMutants_01092017"
#Read in all the fcs files in the directory, with alter.names changing "-" to "."
flowData <- read.flowSet(path = dir1, pattern=".fcs", alter.names=TRUE)
#Read in the sample sheet that should be in each directory that contains the .fcs files.
sample.sheet <- read.csv("/Users/nathanbrandt/Google Drive/Transfer_Folder/RNA_Content_Analysis_SPARQ/mRNA_VariedMedia_KOMutants_01092017_Samplesheet.csv")
#Change names of samples to those specified in the sample sheets
sampleNames(flowData) <- paste(sample.sheet[,5], sample.sheet[,1], sep=" ")#Check how many cells were counted in each fcs file
fsApply(flowData, each_col, length)[1:6]## [1] 10000 10000 10000 10000 10000 10000total <- fsApply(flowData, each_col, length)[1:6] #total counts per sample
#Print the medians of data values for each measurement
fsApply(flowData, each_col, median)## FSC.A SSC.A FL1.A FL2.A FL3.A FL4.A FSC.H SSC.H
## YPD A01 247461.5 21062.0 26379.5 396 1797.0 389.0 433708.0 30627.0
## YPGal A02 225820.0 19790.0 35618.0 484 2286.0 475.0 401796.5 29533.0
## YPGlyc A03 215900.5 16560.5 50794.5 531 2995.0 531.0 387561.5 24925.5
## GlnLim A04 284141.0 18454.5 74616.0 663 4065.0 661.0 465642.5 25300.0
## ProLim A05 254437.0 27396.0 46192.5 567 2746.0 575.5 413497.5 37773.0
## SD A06 145856.5 14510.0 43556.0 447 2541.5 446.0 290563.0 23909.0
## YPD A07 263999.0 19805.5 26656.0 414 1908.0 427.0 483695.0 30454.5
## YPD A08 538803.0 45362.5 17850.5 418 1489.0 420.0 908277.5 65753.0
## YPD A09 271695.0 23343.0 54310.5 562 3160.5 564.0 511383.5 36110.5
## YPD A10 246015.0 25250.0 23754.5 400 1732.5 392.0 469753.5 40270.5
## YPD A11 248471.5 20302.5 66518.0 640 3779.0 652.0 460848.5 30985.0
## YPD B01 200385.0 15790.5 25898.0 387 1778.0 386.0 386821.5 25737.5
## YPGal B02 200208.0 16925.5 47834.5 531 2898.0 534.0 383871.5 27165.5
## YPGlyc B03 177957.5 12765.0 46353.5 514 2804.0 513.0 348630.0 20930.5
## GlnLim B04 263891.5 17296.0 67822.0 652 3817.0 643.0 446893.0 24447.5
## ProLim B05 215714.5 25223.0 22194.5 486 1885.0 513.0 360853.5 35724.0
## SD B06 131860.0 12328.0 45501.5 472 2673.0 476.0 270816.0 21233.0
## YPD B07 199551.0 16580.0 21243.0 371 1602.0 373.0 403491.5 27756.0
## YPD B08 372938.0 41161.5 25234.0 462 1955.0 480.0 689533.5 63825.5
## YPD B09 244791.0 24424.5 24783.0 418 1813.0 429.0 480525.5 39458.5
## YPD B10 224851.5 20480.0 54202.0 548 3117.0 550.0 444644.0 33096.0
## YPD B11 222225.0 18196.5 52644.5 562 3182.0 572.0 442814.5 29939.0
## YPD C01 188290.5 16078.0 59111.5 577 3430.0 585.0 373214.0 26423.0
## YPGal C02 230145.5 18205.0 53007.5 567 3163.0 564.0 424838.0 27740.0
## YPGlyc C03 192794.5 15351.0 49154.0 515 2911.0 516.0 366932.5 24431.5
## GlnLim C04 265228.5 18134.0 69342.5 627 3841.0 623.0 443293.0 25533.0
## ProLim C05 250406.5 32961.5 23091.5 478 1843.0 509.0 407016.5 45924.5
## SD C06 153363.5 13054.5 49268.0 492 2893.0 491.0 309189.0 21494.0
## YPD C07 152322.5 13747.0 39708.0 464 2464.0 468.0 326441.0 23798.5
## YPD C08 220057.0 23337.0 37138.5 468 2404.0 467.0 445143.5 38412.0
## YPD C09 222156.5 17900.0 113851.0 894 6157.0 886.0 449646.0 28833.0
## YPD C10 180246.5 18872.0 32838.5 436 2150.0 433.0 386362.0 32494.5
## YPD C11 181081.5 15007.0 69045.5 630 3926.0 630.0 377145.5 24845.0
## YPD D01 192391.5 16760.0 885.0 226 527.0 219.0 386720.5 27345.0
## YPGal D02 205220.5 17416.5 1223.5 253 588.0 243.0 398938.0 27956.0
## YPGlyc D03 194754.5 14047.0 1040.5 226 518.0 221.0 372197.0 22184.5
## GlnLim D04 270535.0 16687.0 844.0 241 528.0 236.0 455859.5 23053.5
## ProLim D05 116945.0 24591.0 697.0 241 610.0 245.0 191933.0 37020.0
## SD D06 145487.0 13326.5 593.0 201 447.0 195.0 302966.5 22953.0
## YPD D07 180836.5 15335.0 1017.0 249 572.0 240.0 373693.0 26781.5
## YPD D08 403104.0 35485.0 1485.0 297 676.0 286.0 761575.5 58338.0
## YPD D09 279013.5 23764.5 1053.0 247 567.0 239.0 523069.0 36711.0
## YPD D10 203089.0 21184.0 1077.0 249 569.0 246.0 414893.5 36145.0
## YPD D11 243761.5 17100.5 1136.0 257 596.0 249.0 467855.5 27197.5
## YPD E01 164608.0 13603.5 803.5 219 493.0 214.0 341836.5 23278.5
## YPGal E02 193358.0 15863.5 1104.0 243 546.0 239.5 376098.5 25685.0
## YPGlyc E03 182583.0 15284.5 1101.0 235 533.0 226.0 359914.0 24823.5
## GlnLim E04 255122.0 14946.5 959.0 263 564.0 259.0 433239.0 21291.5
## ProLim E05 193809.0 18051.0 866.0 256 581.0 268.0 325045.0 25584.0
## SD E06 151510.0 15056.0 650.0 207 438.0 202.0 308200.5 25756.0
## YPD E07 193099.5 16474.0 930.0 241 522.0 235.0 386092.0 26350.0
## YPD E08 327894.5 34385.0 1545.0 305 715.0 303.0 611145.5 52756.5
## YPD E09 218857.5 21820.5 1326.0 276 652.0 277.0 441446.0 35424.5
## YPD E10 224525.5 19084.5 945.0 237 540.0 233.0 443048.5 31396.5
## YPD E11 236730.0 17832.0 1176.0 268 590.0 259.0 463156.0 28824.5
## YPD F01 204984.5 16578.5 824.0 221 489.0 211.0 395097.0 26649.0
## YPGal F02 210800.5 16829.0 1118.0 246 564.0 242.0 403646.0 27097.5
## YPGlyc F03 220339.0 15037.0 1136.5 240 527.0 229.0 394058.0 22951.5
## GlnLim F04 263436.5 16459.0 838.0 240 534.0 232.0 441144.0 23225.5
## ProLim F05 197411.0 21681.0 845.0 263 591.0 274.0 326340.0 30629.0
## SD F06 192583.0 16905.5 651.0 213 450.5 213.5 336096.5 24260.5
## YPD F07 200580.0 15517.0 919.0 243 536.0 236.0 391528.0 25044.0
## YPD F08 257334.0 24741.0 1069.0 253 564.0 248.0 489627.5 38721.5
## YPD F09 323919.0 24374.5 1013.0 251 554.5 246.0 559719.0 34208.0
## YPD F10 232926.5 20191.0 984.0 241 557.0 235.0 444013.5 31466.5
## YPD F11 210312.0 15228.0 933.0 241 542.0 237.0 413388.0 24134.0
## FL1.H FL2.H FL3.H FL4.H Width Time
## YPD A01 26959.5 282.0 2157.5 469.0 43 191.0
## YPGal A02 37415.5 361.0 2577.0 519.0 42 190.0
## YPGlyc A03 53828.5 421.0 3216.0 579.0 42 190.0
## GlnLim A04 75191.0 525.0 4052.5 660.0 47 329.0
## ProLim A05 43174.5 396.0 2768.0 582.0 46 1948.0
## SD A06 50500.0 366.0 2943.0 515.0 36 195.0
## YPD A07 28689.0 308.0 2240.0 497.0 43 189.0
## YPD A08 19260.0 299.0 1965.0 489.0 53 191.0
## YPD A09 57732.5 449.0 3392.0 602.0 42 192.0
## YPD A10 26354.5 294.0 2132.0 472.0 41 193.0
## YPD A11 71297.5 539.0 4048.0 671.0 42 200.0
## YPD B01 28645.0 283.0 2169.0 459.0 39 195.0
## YPGal B02 53289.5 437.0 3218.0 573.0 39 197.0
## YPGlyc B03 52823.0 428.0 3213.0 565.0 38 197.0
## GlnLim B04 69296.0 520.0 3839.0 649.5 46 317.0
## ProLim B05 23468.5 337.0 2200.0 543.0 44 3681.5
## SD B06 53725.0 399.0 3155.5 537.0 34 190.0
## YPD B07 24685.0 280.0 2048.5 458.5 38 189.0
## YPD B08 28621.5 354.0 2339.0 531.0 47 186.0
## YPD B09 27843.5 317.0 2215.5 496.0 40 194.0
## YPD B10 61238.5 456.0 3516.0 595.0 39 195.0
## YPD B11 60412.0 483.5 3615.0 613.0 39 196.0
## YPD C01 66017.5 487.0 3787.0 620.0 38 194.0
## YPGal C02 57808.5 458.0 3439.0 591.0 42 211.0
## YPGlyc C03 54941.5 424.0 3285.0 566.0 39 204.0
## GlnLim C04 70414.5 495.0 3867.5 632.5 46 368.0
## ProLim C05 23162.5 329.0 2198.0 538.0 46 2872.0
## SD C06 58118.0 415.0 3340.5 552.0 36 206.0
## YPD C07 47515.5 388.0 2948.5 525.0 34 194.0
## YPD C08 43255.0 379.0 2819.0 523.0 39 196.0
## YPD C09 127265.5 834.0 6740.5 913.0 38 206.0
## YPD C10 39049.0 352.0 2611.0 495.5 35 190.0
## YPD C11 79909.0 564.0 4444.5 678.0 36 194.0
## YPD D01 1421.0 145.0 1277.0 343.0 37 200.0
## YPGal D02 1720.0 165.0 1309.0 356.0 39 208.0
## YPGlyc D03 1559.5 146.0 1268.0 347.0 39 226.0
## GlnLim D04 1382.0 153.0 1349.0 364.5 46 452.0
## ProLim D05 1245.0 151.0 1335.0 362.0 38 3422.0
## SD D06 1157.0 126.0 1193.0 311.0 35 200.0
## YPD D07 1548.0 161.0 1299.5 343.0 36 187.0
## YPD D08 2010.0 203.0 1456.0 398.0 47 187.0
## YPD D09 1545.0 158.0 1358.0 365.0 43 222.0
## YPD D10 1587.0 164.0 1325.0 360.0 38 196.0
## YPD D11 1626.0 166.0 1361.0 363.0 41 215.0
## YPD E01 1358.0 135.0 1253.0 331.0 35 198.0
## YPGal E02 1615.5 157.0 1302.5 351.0 39 204.0
## YPGlyc E03 1605.0 151.0 1269.5 343.0 38 210.0
## GlnLim E04 1456.0 166.0 1363.0 376.0 45 462.0
## ProLim E05 1400.0 162.0 1356.0 378.0 43 3579.0
## SD E06 1154.0 132.0 1307.5 321.0 36 199.0
## YPD E07 1479.0 155.0 1296.0 349.0 38 203.0
## YPD E08 2047.5 206.0 1459.0 409.0 45 195.0
## YPD E09 1855.0 186.0 1375.0 378.0 39 201.0
## YPD E10 1456.5 153.0 1296.0 350.0 39 210.0
## YPD E11 1660.0 173.0 1346.0 368.5 40 206.0
## YPD F01 1331.5 140.0 1264.0 337.0 39 207.0
## YPGal F02 1601.0 160.0 1293.0 357.0 40 215.0
## YPGlyc F03 1612.0 152.5 1309.0 353.0 42 259.0
## GlnLim F04 1362.0 152.0 1328.0 363.0 46 914.0
## ProLim F05 1385.0 165.0 1356.0 386.0 44 3494.0
## SD F06 1234.5 135.0 1271.0 340.0 42 269.0
## YPD F07 1473.5 158.0 1280.0 349.0 38 210.0
## YPD F08 1606.5 165.0 1341.5 362.0 42 205.0
## YPD F09 1529.0 159.0 1383.0 372.0 47 274.0
## YPD F10 1517.5 158.0 1307.5 352.0 40 203.0
## YPD F11 1479.0 158.0 1302.0 350.0 39 202.0samples.num <- length(flowData) #number of samples3 Step 3: apply filters to data and generate plots showing the effect on filtering
##Subset the data by applying sequential gates##
#apply doublet gate
flowData.singlets <- Subset(flowData, pg.singlets)
fsApply(flowData.singlets, each_col, length)[1:samples.num]
singlets <- fsApply(flowData.singlets, each_col, length)[1:samples.num]
barplot(singlets/total, ylim=c(0,1), ylab = "Proportion singlet cells", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#apply debris gate
filteredData <- Subset(flowData.singlets, pg.nondebris)
fsApply(filteredData, each_col, length)[1:samples.num]
non.debris <- fsApply(filteredData, each_col, length)[1:samples.num]
barplot(non.debris/total, ylim=c(0,1), ylab = "Proportion singlet and nondebris cells", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#########
#filteredData is the variable name for the data filtered of doublets and debris that are used for all subsequent analyses
##########
#this gate defines nongfp cells
gfp.neg <- Subset(filteredData, pg.nongfp)
fsApply(gfp.neg, each_col, length)[1:samples.num]
non.gfp <- fsApply(gfp.neg, each_col, length)[1:samples.num]
barplot(non.gfp/non.debris, ylim=c(0,1), ylab = "Proportion cells with no GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#this gate defines gfp cells
gfp.pos <- Subset(filteredData, pg.gfp)
fsApply(gfp.pos, each_col, length)[1:samples.num]
gfp.cells <- fsApply(gfp.pos, each_col, length)[1:samples.num]
barplot(gfp.cells/non.debris, ylim=c(0,1), ylab = "Proportion cells with GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))
#this gate defines high GFP cells
gfp.hi <- Subset(filteredData, pg.hi.gfp)
fsApply(gfp.hi, each_col, length)[1:samples.num]
hi.gfp.cells <- fsApply(gfp.hi, each_col, length)[1:samples.num]
barplot(hi.gfp.cells/non.debris, ylim=c(0,1), ylab = "Proportion cells with high GFP", las=2, cex.names = 0.5, names.arg=sampleNames(flowData))filteredData <- flowData4 Step 4: Data analysis
4.1 diagnostic values can be defined for plotting purposes
#define critical values that can superimposed on plots for easy visual comparison
gfp.bg <- 3.9 #a background value for GFP
gfp.wt <- 5.9 #a value for wildtype GFP expression
red.bg <- 3.03 #a background value for the red channel
red.wt <- 3.75 #a value for wildtype Red expression
haploid.fsc <- 6e5 #an empirical value for forward scatter for haploids
diploid.fsc <- 7e5 #an empirical value for forward scatter for diploids
gfp.norm <- 0.935 #an empricial value for gfp expression normalized by forward scatter
red.norm <- 0.57 #an empricial value for red expression normalized by forward scatter
gfp.red.norm <- 1.5 #an empricial value for gfp expression normalized by red channel4.2 Extract data from fcs files to generate statistics and boxplots
#record summary statistics for each sample in a matrix named summary.stats
summary.stats <- matrix(data = NA, nrow = length(filteredData), ncol = 18, dimnames = list(sampleNames(filteredData),c("FSC_median","FSC_mean", "FSC_sd","FL1_median", "FL1_mean","FL1_sd","normalizedGFP_median", "normalizedGFP_mean", "normalizedGFP_sd","FL2_median","FL2_mean","FL2_sd","normalizedRed_median","normalizedRed_mean", "normalizedRed_sd","GFPnormalizedByRed_median", "GFPnormalizedByRed_mean","GFPnormalizedByRed_sd")))
#use the sample containing the minimum number of points after filtering for doublets and debris to define the number of data points retained for all samples
sample.size <- min(fsApply(filteredData, each_col, length))
print(sample.size)## [1] 3931comparison.FSC <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL2 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1NormFsc <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL2NormFsc <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
comparison.FL1NormFL2 <- matrix(data = NA, nrow = sample.size, ncol = length(filteredData), byrow = FALSE,dimnames = NULL)
#for each sample plot a histogram of the normalized data, raw FSC and raw GFP per row
par(mfrow=c(1,2), mar=c(5.1,2.1,2.1,2.1), oma=c(1.5,2,1,1))
#extract data from flowFrames to plot histograms of values and record summary statistics
for (i in 1:length(filteredData)){
temp <- exprs(filteredData[[i]]) #exprs() extracts a matrix of the values from the flowframe
##########################################
#record summary statistics for the sample#
##########################################
#FSC
summary.stats[i,1] <- median(temp[,1])
summary.stats[i,2] <-mean(temp[,1])
summary.stats[i,3] <- sd(temp[,1])
#FL1
summary.stats[i,4] <- median(temp[,3])
summary.stats[i,5] <-mean(temp[,3])
summary.stats[i,6] <- sd(temp[,3])
#FL1 (GFP) divided by FSC
summary.stats[i,7] <- median(temp[,3]/temp[,1])
summary.stats[i,8] <-mean(temp[,3]/temp[,1])
summary.stats[i,9] <- sd(temp[,3]/temp[,1])
#FL2
summary.stats[i,10] <- median(temp[,4])
summary.stats[i,11] <-mean(temp[,4])
summary.stats[i,12] <- sd(temp[,4])
#FL2 (Red) divided by FSC
summary.stats[i,13] <- median(temp[,4]/temp[,1])
summary.stats[i,14] <-mean(temp[,4]/temp[,1])
summary.stats[i,15] <- sd(temp[,4]/temp[,1])
#FL1 (GFP) divided by FL2 (Red)
summary.stats[i,16] <- median(temp[,3]/temp[,4])
summary.stats[i,17] <-mean(temp[,3]/temp[,4])
summary.stats[i,18] <- sd(temp[,3]/temp[,4])
##############################################
#plot histograms of the channels of interest##
##############################################
###############
#Green channel#
###############
#FL1 (GFP)
hist(log10(temp[,3]), br=1000, xlab = "log10(FL1)", main = "FL1")
abline(v=gfp.bg, col="yellow", lty=2, lwd=2)
abline(v=gfp.wt, col="green", lty=2, lwd=2)
legend("topleft", legend=paste("median FL1 = ",round(median(temp[,3]), digits=4),sep=""))
#GFP divided by FSC
hist(temp[,3]/temp[,1], br=500, xlab = "FL1/FSC", main = "FL1/FSC")
abline(v=gfp.norm, col="green", lty=2, lwd=2 )
legend("topleft", legend=paste("median GFP / FSC=",round(median(temp[,3]/temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
###############
#Red channel#
###############
#FL2 (Red)
hist(log10(temp[,4]), br=500, xlab = "log10(FL2)", main = "FL2")
abline(v=red.bg, col="yellow", lty=2, lwd=2)
abline(v=red.wt, col="red", lty=2, lwd=2)
legend("topleft", legend=paste("median FL2=",round(median(temp[,4]), digits=4),sep=""))
#FL2 divided by FSC
hist(temp[,4]/temp[,1], br=500, xlab = "FL2/FSC", main = "FL2/FSC")
abline(v=red.norm, col="red", lty=2, lwd=2 )
legend("topleft", legend=paste("median FL2 / FSC=",round(median(temp[,4]/temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
###############
#Other#########
###############
#FL1 divided by FL2
hist(temp[,4]/temp[,3], br=500, xlab = "FL2/FL1", main = "FL1/FL2")
abline(v=gfp.red.norm, col="purple", lty=2, lwd=2)
legend("topleft", legend=paste("median FL1 / FL2=",round(median(temp[,4]/temp[,3]), digits=4),sep=""))
#FSC
hist(log10(temp[,1]), br=500, xlab = "log10(FSC)", main = "FSC", xlim=c(4,8))
abline(v=haploid.fsc, col="blue", lty=2, lwd=2)
abline(v=diploid.fsc, col="grey", lty=2, lwd=2)
legend("topleft", legend=paste("median FSC=",round(median(temp[,1]), digits=4),sep=""))
mtext(sampleNames(filteredData[i]), outer = TRUE, cex = 1.0)
print("-------------------------------------------------------")
print("-----------------------------------")
print("----------------------")
############################################################
#keep the data set for generating boxplots comparing values#
############################################################
#Note that the amount of data kept for each sample is defined by the lowest count among all the samples.
comparison.FSC[1:sample.size,i] <- temp[1:sample.size,1] #FSC
comparison.FL1[1:sample.size,i] <- temp[1:sample.size,3] #FL1 (GFP)
comparison.FL1NormFsc[1:sample.size,i] <- temp[1:sample.size,3]/temp[1:sample.size,1] #GFP/FSC
comparison.FL2[1:sample.size,i] <- temp[1:sample.size,4] #FL2
comparison.FL2NormFsc[1:sample.size,i] <- temp[1:sample.size,4]/temp[1:sample.size,1] #FL2/FSC
comparison.FL1NormFL2[1:sample.size,i] <- temp[1:sample.size,3]/temp[1:sample.size,4] #FL1/FL2
}
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## [1] "----------------------"par(mfrow=c(1,1)) #change number of plots per row back to standard4.3 Overview of data distributions
par(mar=c(8.1,4.1,4.1,2.1)) #create more space at lower margin
boxplot(comparison.FSC, names=sampleNames(filteredData), notch = TRUE, col = "gray", ylab="FSC", cex.axis=0.5,las=2, outline=F)
abline(h=haploid.fsc, lty=2, col=2)
abline(h=diploid.fsc, lty=2, col=3)
boxplot(comparison.FL1, names=sampleNames(filteredData), notch = TRUE, col = "lightgreen", ylab="FL1", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.bg ,lty=2, lwd=3, col="yellow")
abline(h=gfp.wt, lty = 2, lwd=3, col="green")
boxplot(comparison.FL1NormFsc, names=sampleNames(filteredData), notch = TRUE, col = "green", ylab="FL1/FSC", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.norm, lty=2, lwd=3, col="blue")
boxplot(comparison.FL2, names=sampleNames(filteredData), notch = TRUE, col = "pink", ylab="FL2", cex.axis=0.5,las=2, outline=F)
abline(h=red.bg, lty=2, lwd=3, col="pink")
abline(h=red.wt, lty=2, lwd=3, col="red")
boxplot(comparison.FL2NormFsc, names=sampleNames(filteredData), notch = TRUE, col = "red", ylab="FL2/FSC", cex.axis=0.5,las=2, outline=F)
abline(h=red.norm, lty=2, lwd=3, col="red")
boxplot(comparison.FL1NormFL2, names=sampleNames(filteredData), notch = TRUE, col = "purple", ylab="FL1/FL2", cex.axis=0.5,las=2, outline=F)
abline(h=gfp.red.norm, lty=2, lwd=3, col="purple")
par(mar=c(5.1,4.1,4.1,2.1)) #reset margins to default
#generate a summary table containing all the recorded statistics
print(summary.stats)## FSC_median FSC_mean FSC_sd FL1_median FL1_mean FL1_sd
## YPD A01 247461.5 325429.1 354132.6 26379.5 35736.8193 70772.5209
## YPGal A02 225820.0 272638.4 200464.6 35618.0 44976.5999 158212.2434
## YPGlyc A03 215900.5 269830.7 203114.3 50794.5 63764.0797 56128.1763
## GlnLim A04 284141.0 327723.0 260741.8 74616.0 92374.1620 275126.0777
## ProLim A05 254437.0 349716.1 459818.0 46192.5 65579.0825 188450.5365
## SD A06 145856.5 183067.2 156182.8 43556.0 53067.3393 61458.4557
## YPD A07 263999.0 349311.1 312497.5 26656.0 36276.2868 41464.1285
## YPD A08 538803.0 632557.8 458928.6 17850.5 21715.3572 20934.8332
## YPD A09 271695.0 376840.4 468480.2 54310.5 65968.1099 86009.0989
## YPD A10 246015.0 296439.6 216749.2 23754.5 29674.6353 24693.5577
## YPD A11 248471.5 327895.0 296847.0 66518.0 86289.2955 91430.2186
## YPD B01 200385.0 257669.8 262276.1 25898.0 32865.1972 48169.8808
## YPGal B02 200208.0 242059.3 162897.0 47834.5 56240.1604 43279.5554
## YPGlyc B03 177957.5 226035.9 177811.3 46353.5 58188.0197 49886.4569
## GlnLim B04 263891.5 298695.4 208018.9 67822.0 75744.8475 88954.7177
## ProLim B05 215714.5 280323.7 374446.7 22194.5 63554.5873 276862.0956
## SD B06 131860.0 167487.4 142750.8 45501.5 55604.2905 49927.5804
## YPD B07 199551.0 265179.5 219934.2 21243.0 28525.1239 27600.6290
## YPD B08 372938.0 456310.8 322516.0 25234.0 32398.3281 28181.3476
## YPD B09 244791.0 316887.1 308300.1 24783.0 29997.4233 27636.0128
## YPD B10 224851.5 259793.0 174241.9 54202.0 62177.5437 48138.6414
## YPD B11 222225.0 311247.4 327396.7 52644.5 70360.5466 78385.1320
## YPD C01 188290.5 235483.1 194515.2 59111.5 71653.6920 62003.8228
## YPGal C02 230145.5 273283.0 193364.0 53007.5 61687.1457 67396.5155
## YPGlyc C03 192794.5 237045.7 179097.8 49154.0 59589.0194 50146.5389
## GlnLim C04 265228.5 291576.1 200449.7 69342.5 74445.6298 78620.3413
## ProLim C05 250406.5 323901.5 400831.4 23091.5 37196.3060 128160.3233
## SD C06 153363.5 182151.9 156431.6 49268.0 55745.7613 40850.7012
## YPD C07 152322.5 191770.9 212477.5 39708.0 48382.7642 42764.4103
## YPD C08 220057.0 270048.0 217688.6 37138.5 45165.1916 43773.5583
## YPD C09 222156.5 285703.3 264329.7 113851.0 130973.3346 102086.1480
## YPD C10 180246.5 208287.4 160786.1 32838.5 38207.6041 38903.4570
## YPD C11 181081.5 241863.7 216812.3 69045.5 87275.9821 80586.6143
## YPD D01 192391.5 239535.5 168792.1 885.0 1124.5166 2102.8010
## YPGal D02 205220.5 240864.4 152907.1 1223.5 1412.8025 1226.2160
## YPGlyc D03 194754.5 234838.7 162869.2 1040.5 1254.7565 2184.3896
## GlnLim D04 270535.0 310668.2 250476.6 844.0 1265.1224 4531.7434
## ProLim D05 116945.0 220716.4 420728.2 697.0 5914.3933 82216.7653
## SD D06 145487.0 176927.6 111949.5 593.0 734.2498 720.4033
## YPD D07 180836.5 224266.4 200058.9 1017.0 1228.5581 1462.6317
## YPD D08 403104.0 440491.5 234963.1 1485.0 1691.8805 1352.7182
## YPD D09 279013.5 370669.8 363558.1 1053.0 1345.9993 2104.3730
## YPD D10 203089.0 233484.6 149706.2 1077.0 1220.6901 1066.3363
## YPD D11 243761.5 309847.6 252242.0 1136.0 1374.3864 1500.7368
## YPD E01 164608.0 195727.8 121516.5 803.5 961.1923 1736.1587
## YPGal E02 193358.0 226302.4 134719.0 1104.0 1267.2160 1060.8688
## YPGlyc E03 182583.0 223879.5 153695.1 1101.0 1301.6346 1883.8578
## GlnLim E04 255122.0 289813.4 200607.5 959.0 1375.2787 6983.5733
## ProLim E05 193809.0 236607.4 284636.4 866.0 2493.5127 9689.4102
## SD E06 151510.0 183260.9 114098.0 650.0 786.0555 847.4659
## YPD E07 193099.5 223578.3 129659.3 930.0 1089.9021 1381.6220
## YPD E08 327894.5 374445.2 246019.3 1545.0 1796.7005 2037.9138
## YPD E09 218857.5 264283.8 191497.7 1326.0 1582.5871 1740.9697
## YPD E10 224525.5 256546.9 181550.7 945.0 1167.3423 7214.9293
## YPD E11 236730.0 296523.6 227270.9 1176.0 1410.3773 1566.2941
## YPD F01 204984.5 239400.8 167684.7 824.0 961.7102 1068.7804
## YPGal F02 210800.5 246427.0 197783.4 1118.0 1283.8944 1715.8846
## YPGlyc F03 220339.0 263567.4 182430.5 1136.5 1351.9177 1424.5133
## GlnLim F04 263436.5 290360.9 202343.5 838.0 1269.6201 3864.9961
## ProLim F05 197411.0 268013.7 437902.3 845.0 3847.3967 29201.3278
## SD F06 192583.0 223684.1 125000.4 651.0 833.7101 1935.5293
## YPD F07 200580.0 240806.2 207168.6 919.0 1112.2286 1793.2958
## YPD F08 257334.0 299133.5 180995.1 1069.0 1249.5845 1152.1011
## YPD F09 323919.0 422356.8 363111.9 1013.0 1313.1445 2479.6989
## YPD F10 232926.5 260851.1 148657.8 984.0 1124.9947 1388.6352
## YPD F11 210312.0 262966.3 196401.9 933.0 1120.6578 1082.8639
## normalizedGFP_median normalizedGFP_mean normalizedGFP_sd
## YPD A01 0.106667303 0.109045873 0.039134906
## YPGal A02 0.161018083 0.162351800 0.099516197
## YPGlyc A03 0.232506477 0.236731821 0.070988648
## GlnLim A04 0.259818784 0.263159493 0.187450470
## ProLim A05 0.168070649 0.194304586 0.520404128
## SD A06 0.284865385 0.291907989 0.085066599
## YPD A07 0.100827292 0.104220794 0.035306505
## YPD A08 0.034738141 0.034593954 0.015847330
## YPD A09 0.198891132 0.196658414 0.076599744
## YPD A10 0.099532708 0.102191782 0.036732139
## YPD A11 0.268061348 0.268460295 0.082892593
## YPD B01 0.127812155 0.131122405 0.046895374
## YPGal B02 0.242564638 0.242824257 0.089131494
## YPGlyc B03 0.257442823 0.262173591 0.081239944
## GlnLim B04 0.253164254 0.254797663 0.102813616
## ProLim B05 0.107634116 0.183619891 0.787107402
## SD B06 0.331311963 0.337000325 0.105806117
## YPD B07 0.104423786 0.108394522 0.039200107
## YPD B08 0.071007496 0.072854667 0.030725578
## YPD B09 0.097209203 0.101379123 0.036722242
## YPD B10 0.243397172 0.245156242 0.079500147
## YPD B11 0.236949031 0.237193653 0.075385209
## YPD C01 0.305751429 0.311054910 0.093380508
## YPGal C02 0.236298022 0.233200997 0.087285513
## YPGlyc C03 0.250753871 0.254363345 0.074882878
## GlnLim C04 0.257487112 0.257294397 0.092507833
## ProLim C05 0.080727314 0.115991137 0.208812931
## SD C06 0.306859692 0.313668667 0.104735270
## YPD C07 0.255678643 0.258550664 0.089489889
## YPD C08 0.170212861 0.174073740 0.065609027
## YPD C09 0.493041708 0.499512695 0.157303021
## YPD C10 0.184458282 0.188019389 0.062371825
## YPD C11 0.367538384 0.373558768 0.098477133
## YPD D01 0.004150615 0.005185620 0.012470845
## YPGal D02 0.005660470 0.006222937 0.004605902
## YPGlyc D03 0.004931067 0.005672097 0.004923020
## GlnLim D04 0.002990510 0.003819114 0.005648874
## ProLim D05 0.005185915 0.018601917 0.057685308
## SD D06 0.003619589 0.004632496 0.004702661
## YPD D07 0.005188581 0.005849623 0.004761923
## YPD D08 0.003671655 0.004076350 0.003190524
## YPD D09 0.003417821 0.003931577 0.003455360
## YPD D10 0.005073087 0.005614078 0.004467739
## YPD D11 0.004261277 0.004804962 0.004017164
## YPD E01 0.004505895 0.005238768 0.005031424
## YPGal E02 0.005437608 0.006014042 0.004832057
## YPGlyc E03 0.005540926 0.006221351 0.004965446
## GlnLim E04 0.003622313 0.004383200 0.005040181
## ProLim E05 0.004476235 0.009404649 0.022110358
## SD E06 0.003851310 0.004707150 0.004613428
## YPD E07 0.004478367 0.005164173 0.004635511
## YPD E08 0.004598388 0.005059361 0.004159378
## YPD E09 0.005674290 0.006237335 0.004761678
## YPD E10 0.004020135 0.004546088 0.003965126
## YPD E11 0.004565374 0.005059281 0.004346979
## YPD F01 0.003751024 0.004266672 0.003691587
## YPGal F02 0.005040501 0.005585455 0.004244896
## YPGlyc F03 0.004893339 0.005440535 0.004271223
## GlnLim F04 0.003153063 0.004335401 0.006955729
## ProLim F05 0.004109085 0.009719758 0.034967270
## SD F06 0.003086299 0.003933163 0.004608146
## YPD F07 0.004319214 0.004897557 0.004204470
## YPD F08 0.003916647 0.004501369 0.003790097
## YPD F09 0.002831105 0.003400568 0.004257273
## YPD F10 0.004038773 0.004611645 0.003941844
## YPD F11 0.004006073 0.004689625 0.004111138
## FL2_median FL2_mean FL2_sd normalizedRed_median
## YPD A01 396 471.3543 784.3577 0.0014826735
## YPGal A02 484 620.3280 7152.2699 0.0020922526
## YPGlyc A03 531 621.6524 501.5574 0.0023685957
## GlnLim A04 663 1041.1544 14790.6978 0.0023233902
## ProLim A05 567 1139.1976 18780.9427 0.0022290332
## SD A06 447 506.1296 481.7178 0.0028646236
## YPD A07 414 490.8988 467.1662 0.0014678944
## YPD A08 418 475.7527 414.5439 0.0007686190
## YPD A09 562 668.7004 796.7095 0.0019608726
## YPD A10 400 446.9924 328.0503 0.0015689819
## YPD A11 640 787.7418 749.4506 0.0024846269
## YPD B01 387 437.7123 520.1130 0.0017930651
## YPGal B02 531 596.3892 409.3100 0.0026041441
## YPGlyc B03 514 603.1727 453.8849 0.0027987351
## GlnLim B04 652 757.4603 2121.3505 0.0024632644
## ProLim B05 486 1129.9851 6424.9403 0.0024570854
## SD B06 472 547.8889 492.8618 0.0033912204
## YPD B07 371 431.4317 349.9387 0.0017020074
## YPD B08 462 537.9714 414.0535 0.0012022867
## YPD B09 418 476.5939 459.2885 0.0016020428
## YPD B10 548 606.0192 409.8648 0.0024083048
## YPD B11 562 702.6514 697.7101 0.0024118988
## YPD C01 577 660.5633 511.2339 0.0029067258
## YPGal C02 567 678.3580 5132.8152 0.0024083113
## YPGlyc C03 515 593.1617 430.0949 0.0026175754
## GlnLim C04 627 730.7858 2846.0591 0.0023568015
## ProLim C05 478 990.9231 7936.4659 0.0019951594
## SD C06 492 618.9472 8666.0746 0.0030580176
## YPD C07 464 538.4723 872.5951 0.0029076434
## YPD C08 468 539.3146 437.8464 0.0020765451
## YPD C09 894 1021.4106 750.4366 0.0039030935
## YPD C10 436 472.5052 385.8246 0.0023579990
## YPD C11 630 758.0868 620.5758 0.0033166882
## YPD D01 226 242.3303 177.7777 0.0010570012
## YPGal D02 253 266.8971 185.6168 0.0011540214
## YPGlyc D03 226 249.7480 333.3841 0.0010819871
## GlnLim D04 241 306.0361 776.3837 0.0008654415
## ProLim D05 241 827.0959 11802.6965 0.0018946329
## SD D06 201 214.5788 157.1247 0.0012563761
## YPD D07 249 267.0011 214.8956 0.0012679021
## YPD D08 297 320.9702 234.6751 0.0007299191
## YPD D09 247 273.3012 271.6222 0.0007803299
## YPD D10 249 266.7558 185.6623 0.0011882687
## YPD D11 257 281.2968 240.3391 0.0009514361
## YPD E01 219 231.8768 243.5725 0.0012272265
## YPGal E02 243 257.0453 178.4170 0.0011934402
## YPGlyc E03 235 255.1107 325.8384 0.0011770484
## GlnLim E04 263 329.1531 1403.4954 0.0009964196
## ProLim E05 256 359.1966 711.3159 0.0013454912
## SD E06 207 222.6590 173.9579 0.0012443426
## YPD E07 241 255.4339 194.0989 0.0011640438
## YPD E08 305 333.4169 330.1028 0.0008940573
## YPD E09 276 300.9431 242.3653 0.0011854725
## YPD E10 237 259.4225 772.4222 0.0010076601
## YPD E11 268 289.0674 213.2970 0.0010231445
## YPD F01 221 234.8461 179.3264 0.0010060321
## YPGal F02 246 260.7052 228.2742 0.0010861703
## YPGlyc F03 240 259.5928 207.4178 0.0010212200
## GlnLim F04 240 283.0728 433.8560 0.0008922409
## ProLim F05 263 606.7014 4706.0720 0.0013242036
## SD F06 213 231.5874 237.4530 0.0010352326
## YPD F07 243 261.3950 295.3026 0.0011230889
## YPD F08 253 271.7610 201.8301 0.0009390990
## YPD F09 251 279.7733 373.1259 0.0006871855
## YPD F10 241 258.6708 230.9919 0.0010151210
## YPD F11 241 260.0673 193.0251 0.0010399845
## normalizedRed_mean normalizedRed_sd GFPnormalizedByRed_median
## YPD A01 0.0015902135 0.0009607526 72.61711
## YPGal A02 0.0022390142 0.0039622997 76.42039
## YPGlyc A03 0.0025071501 0.0012384483 99.78778
## GlnLim A04 0.0025110533 0.0025160432 NA
## ProLim A05 0.0031616342 0.0055103497 NA
## SD A06 0.0030165588 0.0014545676 101.23897
## YPD A07 0.0015797619 0.0009376126 70.33997
## YPD A08 0.0008555475 0.0006733025 NA
## YPD A09 0.0020944342 0.0011008661 NA
## YPD A10 0.0016776363 0.0009621465 NA
## YPD A11 0.0025983801 0.0011136277 NA
## YPD B01 0.0019113297 0.0010913416 NA
## YPGal B02 0.0027334874 0.0013299441 92.22782
## YPGlyc B03 0.0029735404 0.0014164913 92.85698
## GlnLim B04 0.0026153587 0.0014093822 NA
## ProLim B05 0.0038782491 0.0284810523 NA
## SD B06 0.0035326601 0.0015579725 99.81450
## YPD B07 0.0018417138 0.0010804804 NA
## YPD B08 0.0013067341 0.0008202830 NA
## YPD B09 0.0017159483 0.0009655888 NA
## YPD B10 0.0025195210 0.0011484756 NA
## YPD B11 0.0025377194 0.0011649178 97.11692
## YPD C01 0.0030099264 0.0012364851 106.04374
## YPGal C02 0.0025335361 0.0013432361 NA
## YPGlyc C03 0.0027750142 0.0013252785 97.11152
## GlnLim C04 0.0025001102 0.0021191462 NA
## ProLim C05 0.0031335212 0.0086386675 NA
## SD C06 0.0031781597 0.0016536277 NA
## YPD C07 0.0030297524 0.0014036196 NA
## YPD C08 0.0022360315 0.0011646399 81.21338
## YPD C09 0.0040299803 0.0015066751 126.96698
## YPD C10 0.0025007379 0.0012514133 NA
## YPD C11 0.0034476817 0.0013070076 112.07977
## YPD D01 0.0012070751 0.0009558041 NA
## YPGal D02 0.0013149301 0.0010175246 NA
## YPGlyc D03 0.0012941996 0.0011300856 NA
## GlnLim D04 0.0010534621 0.0011713956 NA
## ProLim D05 0.0029051738 0.0062873051 NA
## SD D06 0.0014609075 0.0012098473 NA
## YPD D07 0.0014281429 0.0010870407 NA
## YPD D08 0.0008653270 0.0008170308 NA
## YPD D09 0.0009477618 0.0008697711 NA
## YPD D10 0.0013444518 0.0010445057 NA
## YPD D11 0.0011075217 0.0009097318 NA
## YPD E01 0.0013675254 0.0010404165 NA
## YPGal E02 0.0013679246 0.0011391605 NA
## YPGlyc E03 0.0014055736 0.0011872367 NA
## GlnLim E04 0.0011687001 0.0011159154 NA
## ProLim E05 0.0019444869 0.0031176900 NA
## SD E06 0.0014469324 0.0011955271 NA
## YPD E07 0.0013147091 0.0010318910 NA
## YPD E08 0.0010295619 0.0009034055 NA
## YPD E09 0.0013314785 0.0009931054 NA
## YPD E10 0.0011417956 0.0009130994 NA
## YPD E11 0.0011772681 0.0009028711 NA
## YPD F01 0.0011375361 0.0009119842 NA
## YPGal F02 0.0012559769 0.0010025667 NA
## YPGlyc F03 0.0012035282 0.0010442672 NA
## GlnLim F04 0.0011464206 0.0013010038 NA
## ProLim F05 0.0020653174 0.0035101816 NA
## SD F06 0.0011975230 0.0010269866 NA
## YPD F07 0.0012757155 0.0009936070 NA
## YPD F08 0.0010861975 0.0009179274 NA
## YPD F09 0.0008517021 0.0008404694 NA
## YPD F10 0.0011402909 0.0009041940 NA
## YPD F11 0.0012149035 0.0009900743 NA
## GFPnormalizedByRed_mean GFPnormalizedByRed_sd
## YPD A01 Inf NaN
## YPGal A02 Inf NaN
## YPGlyc A03 Inf NaN
## GlnLim A04 NaN NA
## ProLim A05 NaN NA
## SD A06 Inf NaN
## YPD A07 Inf NaN
## YPD A08 NaN NA
## YPD A09 NaN NA
## YPD A10 NaN NA
## YPD A11 NaN NA
## YPD B01 NaN NA
## YPGal B02 Inf NaN
## YPGlyc B03 Inf NaN
## GlnLim B04 NaN NA
## ProLim B05 NaN NA
## SD B06 Inf NaN
## YPD B07 NaN NA
## YPD B08 NaN NA
## YPD B09 NaN NA
## YPD B10 NaN NA
## YPD B11 Inf NaN
## YPD C01 Inf NaN
## YPGal C02 NaN NA
## YPGlyc C03 Inf NaN
## GlnLim C04 NaN NA
## ProLim C05 NaN NA
## SD C06 NaN NA
## YPD C07 NaN NA
## YPD C08 Inf NaN
## YPD C09 Inf NaN
## YPD C10 NaN NA
## YPD C11 Inf NaN
## YPD D01 NaN NA
## YPGal D02 NaN NA
## YPGlyc D03 NaN NA
## GlnLim D04 NaN NA
## ProLim D05 NaN NA
## SD D06 NaN NA
## YPD D07 NaN NA
## YPD D08 NaN NA
## YPD D09 NaN NA
## YPD D10 NaN NA
## YPD D11 NaN NA
## YPD E01 NaN NA
## YPGal E02 NaN NA
## YPGlyc E03 NaN NA
## GlnLim E04 NaN NA
## ProLim E05 NaN NA
## SD E06 NaN NA
## YPD E07 NaN NA
## YPD E08 NaN NA
## YPD E09 NaN NA
## YPD E10 NaN NA
## YPD E11 NaN NA
## YPD F01 NaN NA
## YPGal F02 NaN NA
## YPGlyc F03 NaN NA
## GlnLim F04 NaN NA
## ProLim F05 NaN NA
## SD F06 NaN NA
## YPD F07 NaN NA
## YPD F08 NaN NA
## YPD F09 NaN NA
## YPD F10 NaN NA
## YPD F11 NaN NAsummary.stats <- as.data.frame(summary.stats)4.4 Quantitation of relative FL1 signal
baseline.FL1 <- summary.stats$FL1_median[1]
barplot(summary.stats$FL1_median/baseline.FL1, ylab="Relative FL1 median expression", las=2, cex.names = 0.5, names.arg=sampleNames(filteredData))
4.5 Quantitation of forward scatter
baseline.FSC <- summary.stats$FSC_median[1]
barplot(summary.stats$FSC_median/baseline.FSC, ylab="Relative median FSC", las=2, cex.names = 0.5, names.arg=sampleNames(filteredData))
4.6 Population composition
pop.composition <- rbind(non.gfp/non.debris,gfp.cells/non.debris,hi.gfp.cells/non.debris)
barplot(pop.composition, ylab="Proportion of population", legend=c("No GFP", "Normal GFP", "High GFP"),las=2, cex.names = 0.5,names.arg=sampleNames(filteredData))5 Step 5: Quality control
5.1 Gates
###First flowset
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData.1, xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - singlets gate")
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData.1, displayFilter=TRUE, xlim=c(0,3e6), ylim=c(0,3e5), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - nondebris gate")
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.1, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - non GFP gate")
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.1, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - GFP gate")
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData.1, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "First flowset - high GFP gate")
################
###Second flowset
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData.2, xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - singlets gate")
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData.2, displayFilter=TRUE, xlim=c(0,3e6), ylim=c(0,3e5), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - nondebris gate")
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.2, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - non GFP gate")
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData.2, displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - GFP gate")
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData.2, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = "Second flowset - high GFP gate")
#####Attempted as loop below to plot each one individually and is not working
for (i in 1:length(filteredData)){
#Singlets gate
xyplot(FSC.A~FSC.H, data=flowData[i], xlim=c(0,3e6), ylim=c(0,3e6), filter=pg.singlets, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Debris gate
xyplot(SSC.A ~ FSC.A, data=flowData[i], displayFilter=TRUE, xlim=c(0,3e5), ylim=c(0,3e6), filter=pg.nondebris, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Non-fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData[i], displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.nongfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#Fluorescent population gate
xyplot(FL1.A~FSC.A,data=flowData[i], displayFilter=TRUE, xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
#High fluorescing gate
xyplot(FL1.A~FSC.A,data=flowData[i], xlim=c(0,5e6), ylim=c(0,5e4), filter=pg.hi.gfp, smooth=F, xbin=1024, stat=T, pos=0.5, abs=T, main = sampleNames(flowData)[i])
}5.2 Data transformation for visualization
#In order to look at QC plots the data is transformed using the logicle transform, which is a log transform for high values that transitions to a linear transformation near zero values
#This is simply for visualization purposes
lgcl <- logicleTransform(w = 0.5, t= 10000, m=4.5) #the parameters w,t, and m define the transformation parameters
#Dataset 1 tranformation applied to every channel except width and time
dataLGCLTransform <- transform(filteredData,'FSC.A' = lgcl(`FSC.A`), 'SSC.A' =lgcl(`SSC.A`), 'FL1.A' = lgcl(`FL1.A`), 'FL2.A' = lgcl(`FL2.A`), 'FL3.A' = lgcl(`FL3.A`), 'FL4.A' = lgcl(`FL4.A`),'FSC.H' = lgcl(`FSC.H`),'SSC.H' = lgcl(`SSC.H`),'FL1.H' = lgcl(`FL1.H`),'FL2.H' = lgcl(`FL2.H`),'FL3.H' = lgcl(`FL3.H`),'FL4.H' = lgcl(`FL4.H`)) 5.3 Effect of time
#The effect of time on signal (of which there shouldn't be any)
i <- 1
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
i <- 2
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
i <- 3
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(FL1.A ~ Time, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(150,250), main = sampleNames(filteredData)[i])
}5.4 Plots of FL1 versus FSC
i <- 1
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 2
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 3
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(FL1.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(2,6), sampleNames(filteredData)[i])
}5.5 Plots of FSC versus SSC
i <- 1
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 2
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
i <- 3
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])## Warning: 'filter' must either be a filtersList,filterResultList, a single
## filter object or a named list of filter objects.
####Attempted as loop and will not work
for (i in 1:length(filteredData)){
xyplot(SSC.A ~ FSC.A, data=dataLGCLTransform[i], smooth=F, stat=T, pos=0.5, abs=T, xlim=c(4,8), ylim=c(4,8), sampleNames(filteredData)[i])
}